Multi-object detection and tracking by stereo vision
Pattern Recognition
Joint multitarget object tracking and interaction analysis by a probabilistic bio-inspired model
Proceedings of the first ACM international workshop on Analysis and retrieval of tracked events and motion in imagery streams
MMM'11 Proceedings of the 17th international conference on Advances in multimedia modeling - Volume Part I
COST'10 Proceedings of the 2010 international conference on Analysis of Verbal and Nonverbal Communication and Enactment
Video Behaviour Mining Using a Dynamic Topic Model
International Journal of Computer Vision
Intelligent multi-camera video surveillance: A review
Pattern Recognition Letters
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We introduce a novel behavioral model to describe pedestrians motions, which is able to capture sophisticated motion patterns resulting from the mixture of different categories of random trajectories. Due to its simplicity, this model can be learned from video sequences in a totally unsupervised manner through an Expectation-Maximization procedure.When integrated into a complete multi-camera tracking system, it improves the tracking performance in ambiguous situations, compared to a standard ad-hoc isotropic Markovian motion model. Moreover, it can be used to compute a score which characterizes atypical individual motions.Experiments on outdoor video sequences demonstrate both the improvement of tracking performance when compared to a state-of-the-art tracking system and the reliability of the atypical motion detection.